Our work proposes two training curriculums for a Recurrent Video Object Segmentation engine (RVOS)  [1]}, a neural model for one-shot (or semi-supervised) video object segmentation (VOS).
In this task, a binary mask of an object is provided for a single frame and the goal is predicting the mask of the selected object across the rest of the frames in the video sequence.
RVOS architecture is based on an end-to-end recurrent Conv-LSTM [2]} decoder that tracks objects across frames, with no need of any post-processing.
The recurrent architecture makes RVOS a fast solution for the task, capable of processing more than 20 frames per second [3]}.
RVOS was originally tested on the DAVIS and YouTube-VOS datasets for one-shot video object segmentation.
We show how RVOS struggles with the cars in the KITTI-MOTS dataset [4]}, whose videos are more crowded and challenging than DAVIS or YouTube-VOS.
We improve the off-the-shelf RVOS baseline by modifying its training curriculum in two ways.
First, with a schedule sampling [5]} totally contrary to the one original one in RVOS and, secondly, by gradually increasing the complexity of the task by subsampling video frames at training time.
